Automated driver monitoring systems assess status and changes in a variety of parameters relating to driver performance and attentiveness. They can monitor face angles, i.e., the pitch angle to determine whether the head of a sleepy driver is facing downward, and yaw rotation to determine if a driver's head is turned too far away from the direction of travel. With driver monitoring systems coupled to vehicle instrumentation the system can warn a driver, or passengers or persons remote from the vehicle, when a potentially dangerous situation is sensed. The systems can determine and respond to when the driver may not be paying attention to roadway situations. In some instances, when a potential danger is identified, if the system reaches a determination of driver inattention, automatic controls may prevail over the driver to, for example, decelerate vehicle speed before a danger level escalates.
With driver inattention known to be a significant factor in roadway accidents and deaths, monitoring and addressing occurrences of inattentive driving (e.g., due to drowsiness, medical emergencies or distractions) can mitigate a major cause of vehicle crashes. In addition to improvement of human safety, the ability to accurately monitor and rapidly address inattention can prevent accidents that lead to environmental disasters resulting from multiple modes of transportation.
A common and important indicator of human drowsiness is based on measurement of eye lid openings. While this is particularly relevant to transportation safety, it is also of interest in monitoring a wide variety of human activity affected by drowsiness, including learning. Vision systems incorporating image processing technology temporally monitor eyelid closure to assess drowsiness levels. When a system detects that a driver has become inattentive, it can perform several warning operations, including generation of audible messages and sounds to gain driver attention. Yet the effectiveness and accuracy of measurement may be compromised to the point of not detecting drowsiness in a timely manner or by generating false determinations resulting, for example, from visual occlusions or inaccuracies introduced in the optical measurement process. Given enough time, a system can be calibrated with a high degree of accuracy.